Traditionally, user inputs into computing systems have either been limited to a predefined set of selectable options or require the user to be proficient in a computing language in order for the input to be interpreted correctly. This is due to the relative inability for computing systems to understand natural language, that is, conventional language (such as English or Japanese) that has evolved through use between humans.
To solve this problem, natural language processing methods are being developed in order to allow users to interact with computers in a more natural and effective way. Natural language processing relates to the methods by which computers process and analyse natural language data. This is useful in dialogue systems and information extraction systems. Dialogue systems (or conversational agents) are computer systems that make use of natural language processing to converse with humans in a coherent manner. Information extraction systems make use of natural language processing to extract structured information automatically from unstructured or semi-structured machine-readable text.
One method used in natural language processing is semantic parsing. This extracts the semantic meaning of various words within a sentence. One example of this is shallow semantic parsing. Put simply, shallow parsing refers to the extraction of the ‘who,’ ‘when,’ ‘what,’ ‘where,’ ‘why,’ and ‘how’ elements of an action.
A further method used in natural language processing is syntactic parsing. This extracts the syntactic information from an input sentence. Syntax differs from semantics in that syntax relates to the grammatical structure of the sentence, whilst semantics relates to the meaning of the specific words within the sentence (the words being arranged in the sentence according to the syntax).
Generally, semantic parsing methods tend to rely on syntactic parsing, as the syntax of a sentence helps to inform its semantics.